Eliciting Research Taste in LLMs through Future Research Direction Choice

Research taste is increasingly discussed in the context of AI scientists, yet remains little studied or measured in LLMs. This judgment matters especially in open-ended research, where a direction must be chosen before a verifiable objective is available. We operationalize research taste through one task of the scientific workflow: choosing which future research direction to pursue given a research goal and current results. Rather than predicting downstream scientometric metrics or treating author choices as objectively correct, we evaluate models against human-authored future-work directions and report author alignment, stable divergence, and unstable choice as behavioral measures of research-direction preference. We construct an order-controlled evaluation of 80 pairwise comparisons from 19 ML papers published between December 2025 and February 2026 across four subdomains. Each query presents a research goal, hypotheses tested with key results, and two candidate future directions: one author-recommended direction and one plausible distractor sampled from a similar paper. Across eight LLMs, author alignment ranges from 45.0% to 67.5%, while stable divergence ranges from 15.0% to 36.2%. These divergences largely persist under similarity controls, prompt ablations, and an author-preference framing check, and inter-model agreement suggests that some non-author preferences are shared across models. We characterize what models diverge toward using transition-category labels and selected reasoning examples, finding recurring movement toward scaling/generalization and trade-offs between methodological and infrastructure/data extensions. We view this as an early descriptive evaluation for eliciting research taste profiles in LLMs, and as a step toward understanding and steering their research preferences as they take on more autonomous roles in novel open-ended scientific discovery.